I was born in Viareggio on July 30, 1994. I reached technical maturity diploma in 2013, as an aspiring shipbuilder. Later, I was moved by the passion of business management field merged with my technical background, so i started my university studies in Management Engineering at the University of Pisa. In February 2017, I obtained the Bachelor Degree in Management Engineering, by discussing the topic of Dynamic programming algorithms in the Operational Research field. Then, I obtained the MSc in Management Engineering in May 2019. I presented and discussed my master thesis work, that was the result of a five-month internship in ELIS Consulting & Labs, in Rome, started in November 2018. During this period, I took part in a consulting project, aimed at developing a Deep Learning-based tool for the proper management of sensitive data in the insurance field. I currently work as Data Analyst and Data Scientist at ELIS Consulting&Labs in Rome. In November 2019 I started the Smart Industry PhD Course at Department of Ingegneria dell’Energia, dei Sistemi, del Territorio, Università di Pisa, leveraging a joint-research program with ELIS Consulting&Labs.
Unstructured Data Analytics; Artificial Intelligence; Machine Learning; Deep Learning; Data Science Project Management; Design Science Research, Action Design Research; Design Strategies for Artificial Intelligence artifacts; Digital Innovation; Evidence-Based Management, Industry 4.0
I am intended to develop my PhD work by carrying out activities aimed at applied research, in a business consultancy context, in which I will take part in the implementation of several Data-Science projects. The projects’ activities will be mainly geared towards the development of specific AI-based and analytical artefacts. The focus will be on the formalization of strategies and design methodologies for developing artefacts aimed at extracting valuable knowledge from unstructured data, such as images and text. In this respect, a current area of research interest is the Design Science Research methodology applied to Data Science projects. By referring to the specific business case studies, the research question to be answered is the following: “Is it possible to formalize a general-purpose methodology for designing and developing AI-based artefacts, that is applicable to different industries?” The workplan will be guided by realistic business needs, as explicitly required by the projects’ sponsors, who will be represented by companies operating, for the most part, in the telecommunications, insurance, banking, energy and software-house sectors. In addition, the industrial research activity is aimed at providing, by December of 2022: – An overview of several ways for setting up a general-purpose design methodology for the development of AI-based artefacts; – A well-defined process map for managing and performing projects aimed at developing AI-based artefacts; supported by evidence in real business context applications.